Method of recalibrating a battery energy management processor

1998 ◽  
Vol 70 (1) ◽  
pp. 152
Author(s):  
P Chabbert
Energies ◽  
2021 ◽  
Vol 14 (12) ◽  
pp. 3369
Author(s):  
Maria C. Fotopoulou ◽  
Panagiotis Drosatos ◽  
Stefanos Petridis ◽  
Dimitrios Rakopoulos ◽  
Fotis Stergiopoulos ◽  
...  

This paper introduces a Model Predictive Control (MPC) strategy for the optimal energy management of a district whose buildings are equipped with vertically placed Building Integrated Photovoltaic (BIPV) systems and Battery Energy Storage Systems (BESS). The vertically placed BIPV systems are able to cover larger areas of buildings’ surfaces, as compared with conventional rooftop PV systems, and reach their peak of production during winter and spring, which renders them suitable for energy harvesting especially in urban areas. Driven by both these relative advantages, the proposed strategy aims to maximize the district’s autonomy from the external grid, which is achieved through the cooperation of interactive buildings. Therefore, the major contribution of this study is the management and optimal cooperation of a group of buildings, each of which is equipped with its own system of vertical BIPV panels and BESS, carried out by an MPC strategy. The proposed control scheme consists of three main components, i.e., the forecaster, the optimizer and the district, which interact periodically with each other. In order to quantitatively evaluate the benefits of the proposed MPC strategy and the implementation of vertical BIPV and BESS, a hypothetical five-node distribution network located in Greece for four representative days of the year was examined, followed by a sensitivity analysis to examine the effect of the system configuration on its performance.


Author(s):  
Zheng Pan ◽  
Qihong Xiao ◽  
Yangliang Chen

Dynamic programming algorithms are widely used in motor vehicle fuel cells, and can help battery energy management control to perform error analysis. The paper designs the decision-making process of fuel cell charge and discharge management based on the state transition energy management algorithm, which is used to analyse the cumulative causes of errors and the corresponding results. The article uses simulation software to simulate the algorithm proposed in this paper, and finds that the algorithm is an energy management optimization decision, and the error of the hydrogen consumption obtained by the algorithm relative to the theoretical optimal hydrogen consumption is less than 0.25%.


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